Storing data utilizing a maximum accessibility approach in a dispersed storage network

ABSTRACT

A method for execution by a dispersed storage and task (DST) processing unit of a dispersed storage network includes determining to store data in a storage pool utilizing a maximum accessibility approach. A storage unit performance factor is determined for a plurality of storage units of the storage pool. A number of instances of data storage per storage unit is established based on the storage unit performance factor. A replication factor across the plurality of storage units of the storage pool is also established. A total number C of storage instances for the data is determined based on the number of instances of data storage per storage unit and the replication factor. C number of source names for C storage instances of the data are generated. Storage of the C storage instances of the data is facilitated.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.15/840,151, entitled “STORING DATA UTILIZING A MAXIMUM ACCESSIBILITYAPPROACH IN A DISPERSED STORAGE NETWORK”, filed Dec. 13, 2017, which isa continuation-in-part of U.S. Utility application Ser. No. 15/837,705,entitled “ADDING INCREMENTAL STORAGE RESOURCES IN A DISPERSED STORAGENETWORK”, filed Dec. 11, 2017, which is a continuation-in-part of U.S.Utility application Ser. No. 15/006,735, entitled “MODIFYING STORAGECAPACITY OF A SET OF STORAGE UNITS”, filed Jan. 26, 2016, issued as U.S.Pat. No. 10,079,887 on Sep. 18, 2018, which claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/140,861,entitled “MODIFYING STORAGE CAPACITY OF A STORAGE UNIT POOL”, filed Mar.31, 2015, all of which are hereby incorporated herein by reference intheir entirety and made part of the present U.S. Utility PatentApplication for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention; and

FIG. 10 is a logic diagram of an example of a method of storing datautilizing a maximum accessibility approach in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a storage unit pool 660, the network24 of FIG. 1, and a plurality of distributed storage and task (DST)processing units 1-D. The storage unit pool 660 includes a plurality ofDST execution units. Each DST execution unit includes a plurality ofmemories. Each memory can be implemented utilizing the memory 54 of FIG.2 or another memory device. Each DST execution unit may be implementedutilizing the storage unit 36 of FIG. 1. Each DST processing unit may beimplemented utilizing the computing device 16 of FIG. 1. The DSNfunctions to utilize a maximum accessibility approach for accessingstored data within the storage pool, where the maximum accessibilityapproach includes substantially simultaneous accessing of common datawithin the storage pool by the plurality of DST processing unitsresulting in favorable access performance levels.

In certain situations, some forms of content may need to be distributedto as many entities as possible as rapidly as possible. To maximize theability of a DSN memory to distribute information, slices created from asource can be distributed across a maximum number of independent storageunits, and it may be desirable to accomplish this as quickly aspossible. For example, in a DSN memory with a width of 16, composed of64 storage units, the same source may be encoded on up to (64/16)=4independent “stripes” of storage units, thus making it possible forrequesters to retrieve slices in parallel from up to four times as manylocations as if the source were stored to only a single stripe. Thisconcept can also be extended further if the memory devices internal toeach storage unit have a throughput capacity less than that of thestorage unit. For example, if each of the 64 storage units contain 12memory devices, then up to (12*64/16)=48 instances of the source'sslices can be stored, thereby enabling up to 48 times the throughput foraccess of this source compared to if it is stored on only a width numberof memory devices within a width number of storage units.

The process for distributing an object for maximum accessibility andthroughput begins by first characterizing the ratio between the memorydevice throughput rate and the storage unit throughput rate. Forexample, if the memory device operates at up to 100 MB/s and the storageunit operates at up to 1000 GB/s, then there is no performance advantagein storing more than 10 instances within a single storage unit, and theminimum of this ratio or the number of disks in each storage unit willbe used (e.g. Min(10, 12)=10). The next step is to determine the ratiobetween the number of storage units and the IDA width. In the exampleabove, the 64:16 ratio is 4. Finally, the storage processing unitmultiplies both ratios together to yield the number of instances to bestored, in this case: C=10*4=40. To enable the source to be stored onunique sets of storage units, a computing device can select a sourcename at random, and then adds 1/C of the namespace range C times toproduce 40 unique source names (each 1/C of the namespace apart). Bymaximizing the distance between the names, it can guarantee that theslices fall as far apart as possible, and thus can ensure the sliceswill be on different memory devices as well, with each storage unitreceiving slices from 10 different instances of the source.

Upon retrieval of the source (the metadata for the object may indicatethe object's C value and starting name) the computing device candetermine the alternate related names under which the object can beaccessed and can choose one at random and/or pseudo-randomly. Thus, whenunder high load from many requesters, the load will be evenly spreadacross all storage units and across multiple memory devices within thosestorage units. This approach may be used for data that is under highdemand, and/or when minimizing distribution time is paramount, e.g. whendistributing upgrade packages. To reduce the amount of time it takes asingle writer to write C instances of the object, the computing devicecan write the slice to only a single set of locations and then instructthe receiving storage units to pass along the slice under the permutedname to following sets of storage units, thus avoiding any bottleneck inthe distribution of the many instances of that source's slices. Notethat the number of instances created can in some cases be chosen to beless than C (e.g. an L<C), which is the point at which maximumthroughput can be achieved, but the same approach of deriving variousoff-set source names can still be used (only with some lower number L).

In an example of operation of the utilization of the maximumaccessibility approach, the DST processing unit 1 determines to storedata in the storage pool utilizing the maximum accessibility approach.The determining can include at least one of interpreting a requestand/or identifying the data as frequently accessed by multiplerequesting entities (e.g., many other DST processing units).

Having determined to store the data utilizing the maximum accessibilityapproach, the DST processing unit 1 can determine a storage unitperformance factor for the DST execution units of the storage pool. Thedetermining includes dividing a storage unit throughput rate by a memorydevice throughput rate. For example, the DST processing unit 1 divides astorage unit throughput rate of 1 GB per second by a memory devicethroughput rate of 100 MB per second equaling 10 as the storage unitperformance factor.

Having determined the storage unit performance factor, the DSTprocessing unit 1 can establish a number of instances of storage perstorage unit as a minimum number of the performance factor and a numberof memories of the storage unit. For example, the DST processing unit 1establishes the number of instances as 10 when the performance factor is10 and the number of memories is 12.

Having established the number of instances of storage per storage unit,the DST processing unit 1 can calculate a ratio between the number ofstorage units and an information dispersal algorithm (IDA) width. Forexample, the DST processing unit 1 calculates the ratio as 64 divided by16=4, when the IDA width is 16 and the number of DST execution units is64 (e.g., the storage pool includes DST execution units 1-64).

Having calculated the ratio, the DST processing unit 1 can determine anumber of storage instances (e.g., total number) for the data based onthe number of instances per storage unit and the ratio between thenumber of storage units and the IDA width. For example, the DSTprocessing unit 1 determines the number of storage instances=C=number ofinstances per storage unit X the ratio between the number of storageunits and the IDA width=4×10=40=C.

Having determined the number of storage instances C, the DST processingunit 1 can generate C number of source names for the C instances. Thegenerating can include generating a first source name (e.g., to includea vault identifier associated with the data, a generation number, and arandom object number), and/or generating the remaining source namesspaced apart by 1/C across a DSN address range associated with thestorage pool. The generating can further associate a name of the dataobject with one or more of the first source name and/or the value C tofacilitate subsequent retrieval (e.g., a DSN directory and/or adispersed hierarchical index is updated to associate the name of thedata object with the first source name and the value C).

Having generated the source names, the DST processing unit 1 canfacilitate generation and storage of the C instances of the data. Forexample, the DST processing unit 1 dispersed storage error encodes thedata to produce a plurality of sets of encoded data slices, and for eachinstance, sends, via the network 24, slice access messages 1 thatincludes the plurality sets of encoded data slices to a set of storageunits associated with a set of memory devices corresponding to C of theinstances for storage. For instance, the DST processing unit 1 sends afirst encoded data slice of a set of 16 encoded slices to DST executionunits 1, 17, 33, and 49 where each of the DST execution units 1, 17, 33,and 49 stores the first encoded data slice in 10 of the 12 memories inaccordance with the C source names to effectively replicate the firstencoded data slice across 10 of the 12 memories.

When accessing the data, a DST processing unit can access a directory toidentify the first source name and the value of C, can randomly and/orpseudo-randomly select one instance, can calculate a source namecorresponding to the selected one instance, and can recover the datafrom the one instance. As such, a system performance improvement can beprovided where the plurality of DST processing units substantiallysimultaneously access the C instances of the stored data.

In various embodiments, a processing system of a dispersed storage andtask (DST) processing unit includes at least one processor and a memorythat stores operational instructions, that when executed by the at leastone processor cause the processing system to determine to store data ina storage pool utilizing a maximum accessibility approach. A storageunit performance factor is determined for a plurality of storage unitsof the storage pool. A number of instances of data storage per storageunit is established based on the storage unit performance factor. Areplication factor across the plurality of storage units of the storagepool is also established. A total number C of storage instances for thedata is determined based on the number of instances of data storage perstorage unit and the replication factor. C number of source names for Cstorage instances of the data are generated. Storage of the C storageinstances of the data is facilitated.

In various embodiments, determining to store the data in a storage poolutilizing a maximum accessibility approach includes determining the dataexceeds an access frequency threshold and further includes determiningthat available storage capacity is greater than a minimum storagecapacity threshold level. In various embodiments, determining thestorage unit performance factor includes dividing a storage unitthroughput level by a memory device throughput level. In variousembodiments, the number of instances of data storage per storage unit isestablished as a minimum of a number of memory devices per storage unitand the storage unit performance factor. In various embodiments,establishing the replication factor includes calculating the replicationfactor by dividing a number of storage units of the storage pool by aninformation dispersal algorithm (IDA) width. In various embodiments,determining the total number C of storage instances includes calculatingthe total number C of storage instances by multiplying the number ofinstances of data storage by the replication factor.

In various embodiments, generating C number of source names for the Cstorage instances of the data includes generating a first source namefor the data. A DSN address range for the storage pool is identified.Further source name space is generated by spacing source names apart bythe DSN address range divided by C across the DSN address range startingwith the first source name. A directory to associate the first sourcename and the value of C. In various embodiments, it is determined torecover the data. The directory is accessed to recover the first sourcename and the value of C. One of the C storage instances ispseudo-randomly selected for recovery. A source name corresponding tothe selected one of the C storage instances is calculated based on thefirst source name and the value of C. The data is recovered from theselected one of the C storage instances.

In various embodiments, facilitating storage of the C storage instancesof the data includes, for each storage instance of the C storageinstances, generating a plurality of sets of slice names based on acorresponding sourcing. The data is dispersed storage error encoded toproduce a plurality of sets of encoded data slices corresponding to theplurality of sets of slice names. A plurality of write slice requeststhat includes the plurality of sets of slice names and the correspondingplurality of sets of encoded data slices. The plurality of write slicerequests is sent to a set of storage units of the plurality of storageunits, where the set of storage units is associated with the storageinstance.

FIG. 10 is a flowchart illustrating an example of storing data utilizinga maximum accessibility approach. In particular, a method is presentedfor use in association with one or more functions and features describedin conjunction with FIGS. 1-9, for execution by a dispersed storage andtask (DST) processing unit that includes a processor or via anotherprocessing system of a dispersed storage network that includes at leastone processor and memory that stores instruction that configure theprocessor or processors to perform the steps described below.

The method includes step 668 where a processing system (e.g., of adistributed storage and task (DST) processing unit) determines to storedata in a storage pool utilizing a maximum accessibility approach. Thedetermining can include at least one of interpreting a request,identifying the data as frequently accessed and/or determining the dataexceeds an access frequency threshold, and/or determining that availablestorage capacity is greater than a minimum storage capacity thresholdlevel.

The method continues at step 670 where the processing system determinesa storage unit performance factor for storage units of the storage pool.For example, the processing system divides a storage unit throughputlevel by a memory device throughput level (e.g., a 1 GB write per secondmemory device performance level divided by a 100 MB per second memorydevice throughput level equals a performance factor of 10).

The method continues at step 672 where the processing system establishesa number of instances of data storage per storage unit based on thestorage unit performance factor. For example, the processing systemestablishes the number of instances as a minimum of a number of memorydevices per storage unit and the storage unit performance factor.

The method continues at step 674 where the processing system establishesa replication factor across the storage units of the storage pool. Forexample, the processing system calculates the replication factor bydividing the number of storage units of the storage pool by aninformation dispersal algorithm (IDA) width (e.g., 64 units divided byan IDA width of 16 equals 4).

The method continues at step 676 where the processing system determinesa total number C of storage instances for the data based on the numberof instances of data storage per storage unit and the replicationfactor. For example, the processing system multiplies or otherwiseapplies the number of instances of data storage by the replicationfactor (e.g., 10 instances multiplied by the replication factor of4=40).

The method continues at step 678 where the processing system generates Cnumber of source names for the C storage instances of the data. Forexample, the processing system generates a first source name for thedata, identifies a DSN address range for the storage pool, generatesfurther source name space by spacing source names apart by the DSNaddress range divided by C across the DSN address range starting withthe first source name (e.g., either up or down across the range), andupdates a directory to associate one or more of a name of the data andthe first source name and the value of C.

The method continues at step 680 where the processing system facilitatesstorage of the C storage instances of the data. For example, for eachstorage instance, the processing system generates a plurality of sets ofslice names based on a corresponding sourcing, dispersed storage errorencodes the data to produce a plurality of sets of encoded data slices,generates one or more sets of write slice requests that includes theplurality of sets of slice names and the plurality of sets of encodeddata slices, and sends the one or more write slice requests to a set ofstorage units associated with the storage instance. As an example ofretrieving the data, the processing system accesses the directory torecover the first source name and the value of C, randomly and/orpseudo-randomly selects one storage instance, calculates the source namecorresponding to the one selected instance, and recovers the data fromthe one selected instance.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to determine to store data in a storage pool utilizinga maximum accessibility approach. A storage unit performance factor isdetermined for a plurality of storage units of the storage pool. Anumber of instances of data storage per storage unit is establishedbased on the storage unit performance factor. A replication factoracross the plurality of storage units of the storage pool is alsoestablished. A total number C of storage instances for the data isdetermined based on the number of instances of data storage per storageunit and the replication factor. C number of source names for C storageinstances of the data are generated. Storage of the C storage instancesof the data is facilitated.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing system”, “processingmodule”, “processing circuit”, “processor”, and/or “processing unit” maybe used interchangeably, and may be a single processing device or aplurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing system, processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing system, processing module, module,processing circuit, and/or processing unit. Such a memory device may bea read-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. Note that if theprocessing system, processing module, module, processing circuit, and/orprocessing unit includes more than one processing device, the processingdevices may be centrally located (e.g., directly coupled together via awired and/or wireless bus structure) or may be distributedly located(e.g., cloud computing via indirect coupling via a local area networkand/or a wide area network). Further note that if the processing system,processing module, module, processing circuit, and/or processing unitimplements one or more of its functions via a state machine, analogcircuitry, digital circuitry, and/or logic circuitry, the memory and/ormemory element storing the corresponding operational instructions may beembedded within, or external to, the circuitry comprising the statemachine, analog circuitry, digital circuitry, and/or logic circuitry.Still further note that, the memory element may store, and theprocessing system, processing module, module, processing circuit, and/orprocessing unit executes, hard coded and/or operational instructionscorresponding to at least some of the steps and/or functions illustratedin one or more of the Figures. Such a memory device or memory elementcan be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a storage processingunit that includes a processor, the method comprises: store data in astorage pool by: establishing a number of instances of data storage perstorage unit of a plurality of storage units of the storage pool;establishing a replication factor across the plurality of storage unitsof the storage pool; determining a total number C of storage instancesfor the data based on the number of instances of data storage perstorage unit and the replication factor; and facilitating storage of theC storage instances of the data.
 2. The method of claim 1, furthercomprising: determining to store the data in the storage pool based ondetermining the data exceeds an access frequency threshold anddetermining that available storage capacity is greater than a minimumstorage capacity threshold level.
 3. The method of claim 1, furthercomprising: determining a storage unit performance factor for aplurality of storage units of the storage pool; wherein determining thestorage unit performance factor includes dividing a storage unitthroughput level by a memory device throughput level.
 4. The method ofclaim 1, wherein the number of instances of data storage per storageunit is established as a minimum of a number of memory devices perstorage unit and a storage unit performance factor.
 5. The method ofclaim 1, wherein establishing the replication factor includescalculating the replication factor by dividing a number of storage unitsof the storage pool by an information dispersal algorithm (IDA) width.6. The method of claim 1, wherein determining the total number C ofstorage instances includes calculating the total number C of storageinstances by multiplying the number of instances of data storage by thereplication factor.
 7. The method of claim 1, further comprising:generating C number of source names for the C storage instances of thedata by: generating a first source name for the data; identifying a DSNaddress range for the storage pool; generating further source name spaceby spacing source names apart by the DSN address range divided by Cacross the DSN address range starting with the first source name; andupdating a directory to associate the first source name and a value ofC.
 8. The method of claim 7, further comprising: determining to recoverthe data; accessing the directory to recover the first source name andthe value of C; pseudo-randomly selecting one of the C storage instancesto recover; calculating a source name corresponding to the selected oneof the C storage instances based on the first source name and the valueof C; and recovering the data from the selected one of the C storageinstances.
 9. The method of claim 1, wherein facilitating storage of theC storage instances of the data includes, for each storage instance ofthe C storage instances: generating a plurality of sets of slice namesbased on a corresponding sourcing; dispersed storage error encoding thedata to produce a plurality of sets of encoded data slices correspondingto the plurality of sets of slice names; generating a plurality of writeslice requests that includes the plurality of sets of slice names andthe corresponding plurality of sets of encoded data slices; and sendingthe plurality of write slice requests to a set of storage units of theplurality of storage units, wherein the set of storage units isassociated with the storage instance.
 10. A processing system of astorage processing unit comprises: at least one processor; a memory thatstores operational instructions, that when executed by the at least oneprocessor cause the processing system to: store data in a storage poolby: determining a storage unit performance factor for a plurality ofstorage units of the storage pool; establishing a number of instances ofdata storage per storage unit based on the storage unit performancefactor; establishing a replication factor across the plurality ofstorage units of the storage pool; determining a total number C ofstorage instances for the data based on the number of instances of datastorage per storage unit and the replication factor; and facilitatingstorage of the C storage instances of the data.
 11. The processingsystem of claim 10, wherein the operations instructions furthercomprising determining to store the data in the storage pool based ondetermining the data exceeds an access frequency threshold and furtherdetermining that available storage capacity is greater than a minimumstorage capacity threshold level.
 12. The processing system of claim 10,wherein determining the storage unit performance factor includesdividing a storage unit throughput level by a memory device throughputlevel.
 13. The processing system of claim 10, wherein the number ofinstances of data storage per storage unit is established as a minimumof a number of memory devices per storage unit and the storage unitperformance factor.
 14. The processing system of claim 10, whereinestablishing the replication factor includes calculating the replicationfactor by dividing a number of storage units of the storage pool by aninformation dispersal algorithm (IDA) width.
 15. The processing systemof claim 10, wherein determining the total number C of storage instancesincludes calculating the total number C of storage instances bymultiplying the number of instances of data storage by the replicationfactor.
 16. The processing system of claim 10, generating C number ofsource names for the C storage instances of the data by: generating afirst source name for the data; identifying a DSN address range for thestorage pool; generating further source name space by spacing sourcenames apart by the DSN address range divided by C across the DSN addressrange starting with the first source name; and updating a directory toassociate the first source name and a value of C.
 17. The processingsystem of claim 16, wherein the operational instructions, when executedby the at least one processor, further cause the processing system to:determining to recover the data; accessing the directory to recover thefirst source name and the value of C; pseudo-randomly selecting one ofthe C storage instances to recover; calculating a source namecorresponding to the selected one of the C storage instances based onthe first source name and the value of C; and recovering the data fromthe selected one of the C storage instances.
 18. The processing systemof claim 10, wherein facilitating storage of the C storage instances ofthe data includes, for each storage instance of the C storage instances:generating a plurality of sets of slice names based on a correspondingsourcing; dispersed storage error encoding the data to produce aplurality of sets of encoded data slices corresponding to the pluralityof sets of slice names; generating a plurality of write slice requeststhat includes the plurality of sets of slice names and the correspondingplurality of sets of encoded data slices; and sending the plurality ofwrite slice requests to a set of storage units of the plurality ofstorage units, wherein the set of storage units is associated with thestorage instance.
 19. A computer readable storage medium comprises: atleast one memory section that stores operational instructions that, whenexecuted by a processing system of a storage network that includes aprocessor and a memory, causes the processing system to: store data in astorage pool by: determining a storage unit performance factor for aplurality of storage units of the storage pool; establishing a number ofinstances of data storage per storage unit based on the storage unitperformance factor; establishing a replication factor across theplurality of storage units of the storage pool; determining a totalnumber C of storage instances for the data based on the number ofinstances of data storage per storage unit and the replication factor;and facilitating storage of the C storage instances of the data.
 20. Thecomputer readable storage medium of claim 19, wherein establishing thereplication factor includes calculating the replication factor bydividing a number of storage units of the storage pool by an informationdispersal algorithm (IDA) width.